Open to opportunities

Tanner User

@tanneruser

Message

Data engineer specializing in scalable ETL/ELT and real-time streaming for enterprise analytics.

United States

Message

What I'm looking for

I seek senior data engineering roles building scalable, secure cloud data platforms and real-time streaming systems, collaborating with ML teams to deliver production AI while ensuring compliance and operational excellence.

I am a Data Engineer with over 10 years of experience designing and operating large-scale ETL/ELT pipelines and real-time streaming architectures for enterprise products.

I have built high-throughput ingestion pipelines handling ~1 billion records/day from 400+ sources and architected Lakehouse solutions (Apache Iceberg, Delta Lake on S3) for 180+ PB of data, improving downstream model training efficiency by 40% and reducing analytics query times by up to 55%.

I drive operational excellence through automated monitoring, lineage, and compliance (SOX, GDPR, FINRA), deploy infrastructure with Terraform and Kubernetes to accelerate provisioning by ~30%, and orchestrate 1,000+ active Airflow DAGs to ensure reliable batch workflows.

I partner closely with AI and product teams to integrate data into Agentforce AI and RAG-powered LLM assistants, mentor junior engineers, and deliver production-ready MLOps and model-deployment solutions across AWS, GCP, and Azure.

Experience

Work history, roles, and key accomplishments

Current

Data Engineer

Current

Salesforce

Feb 2018 - Present (7 years 8 months)

Designed and deployed high-throughput ingestion pipelines handling ~1B records/day across Kafka, Flink, Kinesis and Spark, enabling near-real-time analytics for Agentforce AI and Slack Data Cloud. Architected Lakehouse solutions on S3 (Apache Iceberg/Delta) for 180+ PB and optimized ETL into Snowflake/Redshift/BigQuery to reduce critical query times up to 55% while automating monitoring and compli

Kafka Flink Spark Apache Iceberg Delta Lake Snowflake DBT Airflow AWS Kubernetes

Data Engineer

Walmart Global Tech

May 2014 - Feb 2018 (3 years 9 months)

Built and optimized data preprocessing and feature engineering pipelines using Python and Spark to improve model training efficiency for large-scale datasets. Deployed scalable ML models on AWS/Azure and automated workflows with Docker and Airflow to accelerate deployment cycles and increase reproducibility.

Python Spark AWS Azure Docker Airflow Model Interpretability Machine Learning

Education

Degrees, certifications, and relevant coursework

University of New Hampshire

Bachelor of Computer Science, Computer Science

2010 - 2014

Completed a Bachelor of Computer Science at the University of New Hampshire (2010–2014) with coursework and projects focused on ETL/ELT pipelines, data warehousing, real-time streaming, and distributed computing.